Face feature point detection method and device, equipment and storage medium

ABSTRACT

Provided are a face feature point detection method, applied to an image processing device, where the image processing device stores a feature area detection model and a feature point detection model. The method includes: preprocessing a face image to be detected to obtain a preprocessed target face image; performing feature point extraction on the target face image according to the feature area detection model and the feature point detection model to obtain a target feature point coordinate located within a face feature area in the target face image; and performing coordinate transformation on the target feature point coordinate to obtain a face feature point coordinate corresponding to the face image to be detected. Further provided are a face feature point detection device, an equipment and a storage medium.

CROSS-REFERENCE TO RELATED APPLICATION

This is a National stage application, filed under 37 U.S.C. § 371, ofInternational Patent Application No. PCT/CN2019/075801, filed on Feb.22, 2019, which is based on and claims priority to Chinese patentapplication No. 201810909381.3 filed with the China NationalIntellectual Property Administration on Aug. 10, 2018, the disclosure ofwhich is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to the field of face feature pointdetection technologies, and for example, to a face feature pointdetection method and device, an equipment and a storage medium.

BACKGROUND

With the development of science and technology, the face feature pointdetection technologies have been gradually mature and are widely used inthe fields such as artificial intelligence interaction, videoconference, identity authentication and the like. The face feature pointdetection technologies can accurately position parts such as eyebrows,eyes, nose, mouth and the like in the face image by making use ofinformation near the face feature parts and the positional relationshipbetween multiple feature points. Therefore, how to ensure the accuracyof face feature point detection is an important problem for the facefeature point detection technologies.

SUMMARY

The present disclosure provides a face feature point detection methodand device, an equipment and a storage medium.

In an embodiment, the present disclosure provides a face feature pointdetection method, applied to an image processing device. The imageprocessing device stores a feature area detection model for detecting aface feature area and a feature point detection model for detecting aface feature point, and the method includes the steps described below.

A face image to be detected is preprocessed to obtain a preprocessedtarget face image.

Feature point extraction is performed on the target face image accordingto the feature area detection model and the feature point detectionmodel to obtain a target feature point coordinate located within theface feature area in the target face image.

Coordinate transformation is performed on the target feature pointcoordinate to obtain a face feature point coordinate corresponding tothe face image to be detected.

In an embodiment, the present disclosure provides a face feature pointdetection device, applied to an image processing device. The imageprocessing device stores a feature area detection model for detecting aface feature area and a feature point detection model for detecting aface feature point, and the device includes an image preprocessingmodule, a target feature acquisition module and a feature pointcoordinate transformation module.

The image preprocessing module is configured to preprocess a face imageto be detected to obtain the preprocessed target face image.

The target feature acquisition module is configured to perform featurepoint extraction on the target face image according to the feature areadetection model and the feature point detection model to obtain a targetfeature point coordinate face located within a face feature area of thetarget face image.

The feature point coordinate transformation module is configured toperform coordinate transformation on the target feature point coordinateto obtain face feature point coordinate corresponding to the face imageto be detected.

In one embodiment, the present disclosure further provides an equipment.The equipment includes a memory and a processor, where the memory isconfigured to store a computer program, and when executed by theprocessor, the computer program implements the method of any embodimentdescribed above.

In one embodiment, the present disclosure further provides a storagemedium storing a computer-readable program. When the computer-readableprogram is configured to, when executed, implement the method of anyembodiment described above is performed.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram of an image processing device according toan embodiment of the present disclosure;

FIG. 2 is a flowchart of a face feature point detection method accordingto an embodiment of the present disclosure;

FIG. 3 is a flowchart of another face feature point detection methodaccording to an embodiment of the present disclosure;

FIG. 4 is a flowchart of another face feature point detection methodaccording to an embodiment of the present disclosure;

FIG. 5 is a structure diagram of a face feature point detection deviceaccording to an embodiment of the present disclosure;

FIG. 6 is a structure diagram of a target feature acquisition moduleaccording to an embodiment of the present disclosure;

FIG. 7 is a structure diagram of another target feature acquisitionmodule according to an embodiment of the present disclosure; and

FIG. 8 is a structure diagram of an electronic device according to anembodiment of the present disclosure.

REFERENCE LIST

-   10 image processing device-   11 a first memory-   12 a first processor-   13 communication unit-   100 face feature point detection device-   110 image preprocessing module-   120 target feature acquisition module-   130 feature point coordinate transformation module-   121 feature point extraction sub-module-   122 feature area extraction sub-module-   123 feature point screening sub-module-   125 target image acquisition sub-module-   126 image feature acquisition sub-module-   127 image feature transformation sub-module-   810 a second processor-   820 a second memory

DETAILED DESCRIPTION

The embodiments described herein are part, not all, of embodiments ofthe present disclosure. Generally, the components of embodiments of thepresent disclosure described and illustrated in the drawings herein maybe arranged and designed through multiple configurations.

Similar reference numerals and letters indicate similar items in thefollowing drawings, and therefore, once a particular item is defined inone drawing, the item needs no more definition and explanation insubsequent drawings.

Some embodiments of the present disclosure will be described hereinafterin conjunction with the drawings. If not in collision, the embodimentsdescribed herein and the features thereof may be combined with eachother.

With reference to FIG. 1, FIG. 1 is a schematic diagram of an imageprocessing device 10 according to an embodiment of the presentdisclosure. In an embodiment of the present disclosure, the imageprocessing device 10 is configured to perform a face feature pointdetection on a face image, and the image processing device 10 includes aface feature point detection device 100, a first memory 11, a firstprocessor 12 and a communication unit 13. The first memory 11, the firstprocessor 12 and the communication unit 13 are electrically connected toeach other directly or indirectly to implement the transmission andinteraction of data. For example, the electrical connections betweenthese components may be implemented through one or more communicationbuses or signal lines. The face feature point detection device 100includes at least one software function module capable of being storedin the first memory 11 in the form of software or firmware, and thefirst processor 12 performs one or more functional applications and dataprocessing by executing the software function module corresponding tothe face feature point detection device 100 stored in the first memory11.

In this embodiment, the first memory 11 may be configured to store afeature area detection model and a feature point detection model, wherethe feature area detection model is used for detecting a face featurearea in a face image and the feature point detection model is used fordetecting a face feature point in the face image. In an embodiment, theface feature area may include at least one of: a left eyebrow area, aright eyebrow area, a left eye area, a right eye area, a nose area, anda mouth area. In an embodiment, the face feature points may include atleast one of: two corners of each eyebrow, a center point of eacheyebrow, two corners of each eye, a center point of an upper eyelid, acenter point of a lower eyelid, a center point of each eye, a nose tippoint, a nose peak point, two ala nasi points, a nasal septum point, twocorners of a mouth, a center point of the mouth, an uppermost point ofan upper lip and a lowermost point of a lower lip. The feature areadetection model is able to detect all face feature areas actuallyexisting in the face image. For the face feature areas (blocked ormissing) that do not exist in the face image in a case where the faceposture in the face image are abnormal, these nonexistent face featureareas cannot be detected through the feature area detection model. Thefeature point detection model is able to detect face feature pointsactually existing in the face image, but cannot directly ensurepositions of the detected face feature points are correct.

In this embodiment, the feature area detection model is a detectionmodel obtained by training by using a training sample of face image inwhich face feature areas are manually calibrated based on aconvolutional neural network, and the feature point detection model is adetection model obtained by training by using a training sample of faceimage in which face feature points are manually calibrated based on theconvolutional neural network. The feature area detection model and thefeature point detection model may be obtained by performing training bythe image processing device 10 itself, or may be acquired from anexternal device and stored in the first memory 11. The first memory 11may be, but is not limited to, a random access memory, a read-onlymemory, a programmable read-only memory, an erasable programmableread-only memory, and an electrically erasable programmable read-onlymemory. The first memory 11 may be configured to store one or moreapplication programs, and the first processor 12 executes the one ormore application programs after receiving an execution instruction. Inan embodiment, software programs and modules in the first memory 11 mayfurther include an operation system, which may include one or moresoftware components and/or drivers for managing system tasks (such asmemory management, storage device control, power management and thelike) and may communicate with one or more hardware or softwarecomponents to provide a running environment for other softwarecomponents.

In this embodiment, the first processor 12 may be an integrated circuitchip having a signal processing capability. The first processor 12 maybe a general-purpose processor, including a central processing unit(CPU), a network processor (NP) and the like. The first processor 12 canimplement or execute one or more methods, steps and logic block diagramsdisclosed in the embodiments of the present disclosure. Thegeneral-purpose processor may be a microprocessor or any conventionalprocessor.

In this embodiment, the communication unit 13 is configured to establisha communication connection between the image processing device 10 andother external devices via a network, and to perform data transmissionvia the network.

In this embodiment, the image processing device 10 ensures that theimage processing device 10 can accurately position feature points of aface having a normal posture as well as feature points of a face havingan abnormal posture by the face feature point detection device 100stored in the first memory 11, thereby ensuring the detection effect ofthe face feature points.

In an embodiment, the structure shown in FIG. 1 is only one structurediagram of the image processing device 10. The image processing device10 may further include more or less components than the components shownin FIG. 1, or have a configuration different from the configurationshown in FIG. 1. Various components shown in FIG. 1 may be implementedby hardware, software or a combination thereof.

With reference to FIG. 2, FIG. 2 is a flowchart of a face feature pointdetection method according to an embodiment of the present disclosure.In the embodiment of the present disclosure, the face feature pointdetection method 100 is applied to the above image processing device 10,where the image processing device 10 stores a feature area detectionmodel for detecting a face feature area and a feature point detectionmodel for detecting a face feature point. In one embodiment, the facefeature point detection method includes steps 210 to 230.

In step 210, a face image to be detected is preprocessed to obtain apreprocessed target face image.

In this embodiment, when the image processing device 10 obtains a faceimage to be detected, the image processing device 10 can perform sizereduction, size enlargement or other preprocessing on the face image tobe detected to adjust the size of the face image to be detected to asize matched with the feature area detection model and the feature pointdetection model, thereby obtaining a target face image corresponding tothe face image to be detected. In this case, there is a mappingrelationship between the face image to be detected and the target faceimage.

In step 220, feature point extraction of the target face image isperformed according to the feature area detection model and the featurepoint detection model to obtain a target feature point coordinatelocated within the face feature area in the target face image.

In this embodiment, the image processing device 10 may acquire, in thetarget face image, a relevant coordinate of the target feature pointlocated within the face feature area actually existing in the targetface image through the feature area detection model and the featurepoint detection model so as to obtain a coordinate of the correspondingface feature point on the face image to be detected through all thetarget feature point coordinates in the target face image.

In an embodiment, with reference to FIG. 3, FIG. 3 is a flowchart ofanother face feature point detection method according to an embodimentof the present disclosure, and is also one of flowcharts of sub-stepsincluded in the step 220 shown in FIG. 2. In an implementation of thisembodiment, the step 220 may include sub-steps 221, 222 and 223.

In the sub-step 221, all feature point coordinates are extracted fromthe target face image based on the feature point detection model.

In this embodiment, the image processing device 10 may extract allfeature point coordinates existing in the target face image from thetarget face image through the feature point detection model.

In the sub-step 222, feature point coordinates of all the face featureareas are extracted from the target face image based on the feature areadetection model.

In this embodiment, the image processing device 10 may extract all facefeature areas existing in the target face image from the target faceimage and the feature area coordinate corresponding to each face featurearea through the feature area detection model.

In the sub-step 223, a target feature point coordinate located in theface feature area corresponding to each feature area coordinate isscreened out from all feature point coordinates according to theobtained feature area coordinates.

In this embodiment, the image processing device 10 performs coordinatescreening on all the acquired feature point coordinates through theacquired feature area coordinates of all face feature areas to obtainthe target feature point coordinate located within each face featurearea in the target face image.

In an embodiment, the step in which the target feature point coordinatelocated in the face feature area corresponding to each feature areacoordinate is screened out from all feature point coordinates accordingto the obtained feature area coordinates includes the steps describedbelow.

Each of the feature point coordinates is compared with a feature areacoordinate corresponding to each of the face feature areas.

If one or more feature point coordinates are located in the face featureareas, the one or more feature point coordinates are used as the one ormore target feature point coordinates in the target face image.

With reference to FIG. 4, FIG. 4 is a flowchart of another face featurepoint detection method according to an embodiment of the presentdisclosure, and is also one of flowcharts of sub-steps included in thestep 220 shown in FIG. 2. In another implementation of this embodiment,at least one feature point detection model is provided, and each of theat least one feature point detection model corresponds to one facefeature area extracted by the feature area detection model. For example,when the feature area detection model is able to extract 6 face featureareas from one face image having a normal face posture, each facefeature area corresponds to one feature point detection model, and thefeature point detection model corresponding to each face feature area isonly used for detecting a feature point existing on part of the faceimage, the part of the face image corresponding to each face featurearea. In this case, the step 220 may include sub-steps 225, 226 and 227.

In the sub-step 225, target images corresponding to all face featureareas in the target face image are extracted from the target face imageaccording to the feature area detection model.

In this embodiment, the image processing device 10 extracts feature areacoordinates of all face feature areas existing in the target face imagethrough the feature area detection model, and segments a target imagecorresponding to one or more face feature areas from the target faceimage based on one or more feature area coordinates.

In the sub-step 226, according to a corresponding relationship betweenthe face feature areas and the at least one feature point detectionmodel, a respective one of the at least one feature point detectionmodel matched with each of the face feature areas is selected, andfeature point extraction is performed on a target image corresponding toeach of the face feature areas, so as to obtain an image feature pointcoordinate in each of the target images.

In this embodiment, according to the corresponding relationship betweenthe face feature areas and the feature point detection models, the imageprocessing device 10 selects a respective feature point detection modelcorresponding to each of one or more face feature areas in the targetface image and performs feature point extraction on a target imagecorresponding to the each of the one or more face feature areas, so asto obtain an image feature point coordinate in each target image. Forexample, if the face feature areas existing in one target face image arefeature area A, feature area B and feature area C respectively, targetimages corresponding to the three face feature areas in the target faceimage are image A1, image B1 and image C1 respectively, where thefeature point detection model corresponding to the feature area A ismodel 1, the feature point detection model corresponding to the featurearea B is model 2, and the feature point detection model correspondingto the feature area C is model 3. In this case, the image processingdevice 10 adjusts a size of the image A1 to a size matched with themodel 1 and performs feature point extraction on the adjusted image A1by the model 1 to obtain an image feature point coordinate in theadjusted image A1; adjusts a size of the image B1 to a size matched withthe model 2 and performs feature point extraction on the adjusted imageB1 by the model 2 to obtain an image feature point coordinate in theadjusted image B1; and adjusts a size of the image C1 to a size matchedwith the model 3 and performs feature point extraction on the adjustedimage C1 by the model 3 to obtain an image feature point coordinate inthe adjusted image C1.

In step 227, according to a mapping relationship between the target faceimage and at least one target images, coordinate transformation isperformed on each image feature point coordinate to obtain a targetfeature point coordinate in the target face image.

In this embodiment, in a case where the image processing device 10segments at least one target image from the target face image, aposition mapping relationship corresponding to the at least one targetimages in the target face image can be obtained. After obtaining theimage feature point coordinate in each target image, the imageprocessing device 10 performs, according to a mapping relationshipbetween the target face image and the at least one target images, thecoordinate transformation on the image feature point coordinate of eachimage feature point coordinate to obtain a target feature pointcoordinate in the target face image.

With reference to FIG. 2, in the step 230, the coordinate transformationis performed on the target feature point coordinate to obtain the facefeature point coordinate corresponding to the face image to be detected.

In this embodiment, the step in which the coordinate transformation isperformed on the obtained target feature point coordinate to obtain theface feature point coordinate corresponding to the face image to bedetected includes the following.

According to a mapping relationship between the face image to bedetected and the target face image, the coordinate transformation isperformed on each target feature point coordinate in the target faceimage to obtain a corresponding face feature point coordinate.

In this embodiment, the image processing device 10 defines thedistribution of effective feature points in the face image to bedetected through the feature area detection model. That is, theeffective feature points should be located within the corresponding facefeature area, and the face feature point coordinate located within theface feature area is obtained through the feature point detection model,thereby ensuring the detection effect of the face feature point throughthe effective face feature point coordinate located within the facefeature area. The face feature point detection method can not onlyaccurately position feature points of the face having a normal posture,but also accurately position feature points of the face having anabnormal posture.

With reference to FIG. 5, FIG. 5 is a structure diagram of a facefeature point detection device 100 according to an embodiment of thepresent disclosure. In the embodiment of the present disclosure, theface feature point detection device 100 is applied to the above imageprocessing device 10, where the image processing device 10 stores afeature area detection model for detecting a face feature area and afeature point detection model for detecting a face feature point. Theface feature point detection device 100 includes an image preprocessingmodule 110, a target feature acquisition module 120 and a feature pointcoordinate transformation module 130.

The image preprocessing module 110 is configured to preprocess a faceimage to be detected to obtain the preprocessed target face image.

In this embodiment, the image preprocessing module 110 may perform thestep 210 shown in FIG. 2, and the execution process may refer to thedescription of the step 210.

The target feature acquisition module 120 is configured to performfeature point extraction on the target face image according to thefeature area detection model and the feature point detection model toobtain a target feature point coordinate located within the face featurearea in the target face image.

In an embodiment, with reference to FIG. 6, FIG. 6 is a structurediagram of a target feature acquisition module according to anembodiment of the present disclosure, and is also one of structurediagrams of the target feature acquisition module 120 shown in FIG. 5.In an implementation of this embodiment, the target feature acquisitionmodule 120 includes a feature point extraction sub-module 121, a featurearea extraction sub-module 122, and a feature point screening sub-module123.

The feature point extraction sub-module 121 is configured to extract allfeature point coordinates from the target face image based on thefeature point detection model.

The feature area extraction sub-module 122 is configured to extractfeature area coordinates of all face feature areas from the target faceimage based on the feature area detection model.

The feature point screening sub-module 123 is configured to screen out,according to the obtained feature area coordinates, a target featurepoint coordinate located in the face feature areas corresponding to thefeature area coordinates from all feature point coordinates.

In this embodiment, the feature point extraction sub-module 121, thefeature area extraction sub-module 122, and the feature point screeningsub-module 123 may perform sub-steps 221, 222 and 223 shown in FIG. 3respectively, and the execution process may refer to the description ofthe sub-steps 221, 222 and 223.

With reference to FIG. 7, FIG. 7 is a structure diagram of anothertarget feature acquisition module according to an embodiment of thepresent disclosure, and is also one of block diagrams of the targetfeature acquisition module 120 shown in FIG. 5. In an embodiment of thepresent embodiment, at least one feature point detection model isprovided, and each feature point detection model corresponds to one facefeature area extracted by the feature area detection model. The targetfeature acquisition module 120 includes a target image acquisitionsub-module 125, an image feature acquisition sub-module 126, and animage feature transformation sub-module 127.

The target image acquisition sub-module 125 is configured to extract alltarget images corresponding to all face feature areas in the target faceimage according to the feature area detection model.

The image feature acquisition sub-module 126 is configured to, accordingto a corresponding relationship between the face feature areas and theat least one feature point detection model, select a respective one ofthe at least one feature point detection model matched with each of theface feature areas and perform feature point extraction on a targetimage corresponding to each of the face feature areas, so as to obtainan image feature point coordinate in each of the target images.

The image feature transformation sub-module 127 is configured to,according to a mapping relationship between the target face image and atleast one of the target images, perform coordinate transformation on theimage feature point coordinate to obtain a target feature pointcoordinate in the target face image.

In this embodiment, the target image acquisition sub-module 125, theimage feature acquisition sub-module 126, and the image featuretransformation sub-module 127 may perform sub-steps 225, 226 and 227shown in FIG. 4 respectively, and the execution process may refer to thedescription of the sub-steps 225, 226 and 227.

With continued reference to FIG. 5, the feature point coordinatetransformation module 130 is configured to perform the coordinatetransformation on the obtained target feature point coordinate to obtaina face feature point coordinate corresponding to the face image to bedetected.

In this embodiment, the feature point coordinate transformation module130 may perform the step 230 shown in FIG. 2, and the execution processmay refer to the description of the step 230.

In an embodiment, the present disclosure further provides an equipment.With reference to FIG. 8, the equipment includes a second processor 810and a second memory 820, where the second memory 820 is configured tostore a computer program, and when executed by the second processor 810,the computer program implements the method of any embodiment describedabove.

In an embodiment, the present disclosure further provides a storagemedium storing a computer-readable program. The computer-readableprogram is configured to, when executed, implement the method of anyembodiment described above.

The storage medium is any one or more of various types of memory devicesor storage devices. The term “storage medium” is intended to include: aninstallation medium, a read-only memory (ROM) such as a compact discread-only memory (CD-ROM), a floppy disk or a magnetic tape device; acomputer system memory or a random access memory such as a dynamicrandom access memory (DRAM), a double data rate random access memory(DDR RAM), a static random-access memory (SRAM), an extended data outputrandom access memory (EDO RAM) or a Rambus random access memory (RambusRAM); a non-volatile memory such as a flash memory or a magnetic media(e.g., a hard disk or an optical storage); and a register or othersimilar types of memory components. The storage medium may furtherinclude other types of memories or combinations thereof.

The method extracts extractable face feature areas in the target faceimage through the feature area detection model, extracts extractabletarget feature point coordinates located within the face feature areasin the target face image through the feature point detection model, andthen acquire coordinate information of one or more face feature pointsin the face image to be detected based on the obtained target featurepoint coordinates, thereby achieving accurate positioning of facefeature points on the face image having the normal face posture or theface image having the abnormal face posture, and ensuring the detectioneffect of the face feature points.

In summary, in the face feature point detection method and device, theequipment and the storage medium provided by the embodiments of thepresent disclosure, the face feature point detection method can not onlyaccurately position feature points of the face having the normalposture, but also accurately position the feature points of the facehaving the abnormal posture, thus ensuring the detection effect of theface feature points. The method is applied to an image processingdevice. The image processing device stores the feature area detectionmodel for detecting the face feature area and the feature pointdetection model for detecting the face feature point. First, the methodpreprocesses the face image to be detected to obtain the preprocessedtarget face image. Then the method performs feature point extraction onthe target face image according to the feature area detection model andthe feature point detection model to obtain the target feature pointcoordinates located within the face feature areas in the target faceimage. Finally, the method performs the coordinate transformation on thetarget feature point coordinates to obtain the face feature pointcoordinates corresponding to the face image to be detected. The methodextracts extractable face feature areas in the target face image havingthe normal face posture or in the target face image having the abnormalface posture through the feature area detection model, extractsextractable target feature point coordinates located within the facefeature areas in the target face image through the feature pointdetection model, and then acquire coordinate information of one or moreface feature points in the face image to be detected based on theobtained target feature point coordinates, thereby achieving accuratepositioning of face feature points on the face image having the normalface posture or the face image having the abnormal face posture,ensuring the detection effect of the face feature points.

What is claimed is:
 1. A face feature point detection method, applied toan image processing device, the method comprising: preprocessing a faceimage to be detected to obtain a preprocessed target face image;performing feature point extraction on the target face image accordingto a feature area detection model and a feature point detection model toobtain a target feature point coordinate located within a face featurearea in the target face image; and performing coordinate transformationon the target feature point coordinate to obtain a face feature pointcoordinate corresponding to the face image to be detected, wherein thefeature area detection model for detecting a face feature area and thefeature point detection model for detecting a face feature point arestored in the image processing device; and wherein the performingfeature point extraction on the target face image according to thefeature area detection model and the feature point detection model toobtain the target feature point coordinate located within the facefeature area in the target face image comprises: extracting all featurepoint coordinates from the target face image based on the feature pointdetection model; and extracting feature area coordinates of all facefeature areas from the target face image based on the feature areadetection model.
 2. The method of claim 1, wherein the performingfeature point extraction on the target face image according to thefeature area detection model and the feature point detection model toobtain the target feature point coordinate located within the facefeature area in the target face image further comprises: screening out,according to the obtained feature area coordinates, a target featurepoint coordinate located in each of the face feature areas correspondingto the feature area coordinates from all the feature point coordinates.3. The method of claim 2, wherein the screening out, according to theobtained feature area coordinates, the target feature point coordinatelocated in each of the face feature areas corresponding to the featurearea coordinates from all feature point coordinates comprises: comparingeach of the feature point coordinates with a feature area coordinatecorresponding to each of the face feature areas; and in response to acomparison result of one or more feature point coordinates located inthe each of the face feature areas, using the one or more feature pointcoordinates as one or more target feature point coordinates in thetarget face image.
 4. The method of claim 1, wherein the imageprocessing device comprises at least one feature point detection model,and each of the at least one the feature point detection modelcorresponds to one face feature area extracted by the feature areadetection model.
 5. The method of claim 4, wherein the performingfeature point extraction on the target face image according to thefeature area detection model and the feature point detection model toobtain a target feature point coordinate located within the face featurearea in the target face image comprises: extracting, from the targetface image, target images corresponding to all face feature areas in thetarget face image according to the feature area detection model;selecting a respective one of the at least one feature point detectionmodel matched with each of the face feature areas according to acorresponding relationship between the face feature areas and the atleast one feature point detection model, and performing feature pointextraction on a target image corresponding to the each of the facefeature areas, so as to obtain an image feature point coordinate in eachof the target images; and according to a mapping relationship betweenthe target face image and at least one of the target images, performingcoordinate transformation on the image feature point coordinate toobtain a target feature point coordinate in the target face image. 6.The method of claim 1, wherein the performing the coordinatetransformation on the target feature point coordinate to obtain the facefeature point coordinate corresponding to the face image to be detectedcomprises: according to a mapping relationship between the face image tobe detected and the target face image, performing coordinatetransformation on each target feature point coordinate in the targetface image to obtain the corresponding face feature point coordinate. 7.A face feature point detection device, applied to an image processingdevice, the face feature point detection device comprising: a memory, aprocessor and a computer program stored in the memory and executable bythe processor, wherein the processor, when executing the computerprogram, implements: preprocessing a face image to be detected to obtaina preprocessed target face image; performing feature point extraction onthe target face image according to a feature area detection model and afeature point detection model to obtain a target feature pointcoordinate located within a face feature area in the target face image;and performing coordinate transformation on the target feature pointcoordinate to obtain a face feature point coordinate corresponding tothe face image to be detected, wherein the feature area detection modelfor detecting a face feature area and the feature point detection modelfor detecting a face feature point are stored in the image processingdevice; and wherein the performing feature point extraction on thetarget face image according to the feature area detection model and thefeature point detection model to obtain the target feature pointcoordinate located within the face feature area in the target face imagecomprises: extracting all feature point coordinates from the target faceimage based on the feature point detection model; and extracting featurearea coordinates of all face feature areas from the target face imagebased on the feature area detection model.
 8. The device of claim 7,wherein the performing feature point extraction on the target face imageaccording to the feature area detection model and the feature pointdetection model to obtain the target feature point coordinate locatedwithin the face feature area in the target face image further comprises:screening out, according to the obtained feature area coordinates, atarget feature point coordinate located in each of the face featureareas corresponding to the feature area coordinates from all the featurepoint coordinates.
 9. The device of claim 8, wherein the screening out,according to the obtained feature area coordinates, the target featurepoint coordinate located in each of the face feature areas correspondingto the feature area coordinates from all feature point coordinatescomprises: comparing each of the feature point coordinates with afeature area coordinate corresponding to each of the face feature areas;and in response to a comparison result of one or more feature pointcoordinates located in the each of the face feature areas, using the oneor more feature point coordinates as one or more target feature pointcoordinates in the target face image.
 10. The device of claim 7, whereinthe image processing device comprises at least one feature pointdetection model, and each of the at least one the feature pointdetection model corresponds to one face feature area extracted by thefeature area detection model.
 11. The device of claim 10, wherein theperforming feature point extraction on the target face image accordingto the feature area detection model and the feature point detectionmodel to obtain a target feature point coordinate located within theface feature area in the target face image comprises: extracting, fromthe target face image, target images corresponding to all face featureareas in the target face image according to the feature area detectionmodel; selecting a respective one of the at least one feature pointdetection model matched with each of the face feature areas according toa corresponding relationship between the face feature areas and the atleast one feature point detection model, and performing feature pointextraction on a target image corresponding to the each of the facefeature areas, so as to obtain an image feature point coordinate in eachof the target images; and according to a mapping relationship betweenthe target face image and at least one of the target images, performingcoordinate transformation on the image feature point coordinate toobtain a target feature point coordinate in the target face image. 12.The device of claim 7, wherein the performing the coordinatetransformation on the target feature point coordinate to obtain the facefeature point coordinate corresponding to the face image to be detectedcomprises: according to a mapping relationship between the face image tobe detected and the target face image, performing coordinatetransformation on each target feature point coordinate in the targetface image to obtain the corresponding face feature point coordinate.13. A non-transitory computer-readable storage medium storing acomputer-readable program, wherein the computer-readable program isconfigured to, when executed, implement the method of claim
 1. 14. Themethod of claim 2, wherein the performing the coordinate transformationon the target feature point coordinate to obtain the face feature pointcoordinate corresponding to the face image to be detected comprises:according to a mapping relationship between the face image to bedetected and the target face image, performing coordinate transformationon each target feature point coordinate in the target face image toobtain the corresponding face feature point coordinate.
 15. The methodof claim 3, wherein the performing the coordinate transformation on thetarget feature point coordinate to obtain the face feature pointcoordinate corresponding to the face image to be detected comprises:according to a mapping relationship between the face image to bedetected and the target face image, performing coordinate transformationon each target feature point coordinate in the target face image toobtain the corresponding face feature point coordinate.
 16. The methodof claim 4, wherein the performing the coordinate transformation on thetarget feature point coordinate to obtain the face feature pointcoordinate corresponding to the face image to be detected comprises:according to a mapping relationship between the face image to bedetected and the target face image, performing coordinate transformationon each target feature point coordinate in the target face image toobtain the corresponding face feature point coordinate.
 17. The methodof claim 5, wherein the performing the coordinate transformation on thetarget feature point coordinate to obtain the face feature pointcoordinate corresponding to the face image to be detected comprises:according to a mapping relationship between the face image to bedetected and the target face image, performing coordinate transformationon each target feature point coordinate in the target face image toobtain the corresponding face feature point coordinate.
 18. The deviceof claim 8, wherein the feature point coordinate transformation moduleis configured to: according to a mapping relationship between the faceimage to be detected and the target face image, perform the coordinatetransformation on the target feature point coordinate in the target faceimage to obtain a corresponding face feature point coordinate.
 19. Thedevice of claim 9, wherein the feature point coordinate transformationmodule is configured to: according to a mapping relationship between theface image to be detected and the target face image, perform thecoordinate transformation on the target feature point coordinate in thetarget face image to obtain a corresponding face feature pointcoordinate.